How Multi-Step versus One-Step Preparation Method Affects the Physicochemical Properties and Transfection Efficiency of DNA/DODAB:MO Lipoplexes
Bibliographic record
Abstract
The consequences for the transfection efficiencies of different lipoplexes preparation methods, largely remain to be explored, but the knowledge of how different experimental approaches can affect the physicochemical properties and transfection efficiency is essential for a proper tailoring of transfection complexes to particular applications. Therefore, the influence of the number of mixing steps (one-step addition versus multi-step addition of liposomes to plasmid DNA (pDNA)) and lipoplex incubation temperature on the final physicochemical properties and transfection efficiency of pDNA/ Dioctadecyldimethylammonium Bromide (DODAB):1-monooleoyl-rac-glycerol (MO) complexes was studied in three distinct DODAB:MO molar ratios: 4:1, 2:1 and 1:1. Dynamic Light Scattering (DLS), Zeta (ζ) Potential, Ethidium Bromide (EtBr) exclusion assays were used to assess the formation, structure and destabilization of the lipoplexes, whereas in vitro transfection assays with pSV-β-gal plasmid DNA were performed to evaluate their transfection efficiency on the 293T mammalian cell line. Results indicate that the morphology of pDNA/DODAB:MO complexes is dependent on the lipoplex preparation method, resulting in particles of distinct size, surface charge and membrane fluidity. These variations are visible during the complexation dynamics of pDNA and continue throughout the profile of pDNA release from pDNA/DODAB:MO lipoplexes upon incubation with Heparin (HEP), as well as in the in vitro transfection assays. The stepwise addition of DODAB:MO vesicles to pDNA decreases the transfection efficiency of the lipoplexes, while the effect of the lipoplex preparation methods is dependent on the MO content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".